题目：Privacy Preserving Smart Metering with Data Authentication and False Data Injection Protection
报告人：Dr. Fengjun Li, University of Kansas
The core of the smart grid relies on the ability of transmitting real-time metering data and control commands efficiently and reliably. Secure in-network data aggregation approaches have been introduced to fulfill the goal in smart grid neighborhood area networks (NAN) by aggregating the data on-the-fly via meters en-route to the end concentrator. In the aggregation, homomorphic encryption schemes have been adopted to protect user’s privacy from being learnt from the fine-grained data by utilities or other third-party services. However, the malleable property of homomorphic encryption operations makes it difficult to identify misbehaving meters from which false data may be injected via accidental errors or malicious attacks.
In this talk, I will briefly discuss solutions for data integrity and false data detection that are compatible with existing homomorphic encryption-based data aggregation schemes. I will first introduce a homomorphic signature scheme with batch verification capability to support end-to-end verification at the concentrator against unintentional errors. Next, to detect falsified data injected by malfunctioning or malicious meters, I will present an incremental verification protocol. With a light-weight dynamic grouping scheme and a data re-encryption scheme, the incremental verification will be trigged in an ex post facto basis and thus achieves a desirable efficiency.
Fengjun Li is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas. She earned her Ph.D. in Information Sciences and Technology from the Pennsylvania State University in 2010, and an M. Phil. in Information Engineering from the Chinese University of Hong Kong in 2004. Her research interests focus on security and privacy issues in distributed information systems, database systems, and communication networks.